Pharma’s embrace of real-world data is shaping the future of drug development

In recent years, pharmaceutical companies have leaned into real-world evidence derived from real-world data to support the drug development process, from clinical trials to applications for new drugs and biologics. One study found that 85% of FDA-approved new drug and biologics license applications incorporate RWE in some form. By tapping into data from everyday healthcare settings-such as electronic health records (EHRs), claims data, and wearables-RWE offers a valuable complement to traditional evidence from randomized controlled trials (RCTs). Although RCTs remain the gold standard in research, RWD can help make the development process faster and better aligned with real-world patient experiences.

Understanding real-world data and real-world evidence

Real-world data (RWD) refers to data about patient health status or healthcare delivery collected from everyday settings outside controlled clinical trials. This data is gathered from various sources, including electronic health records (EHRs), medical claims, product or disease registries, wearable devices, health surveys, and more. Real-world evidence (RWE) is the clinical evidence derived from analyzing RWD. It provides a broader perspective on how treatments and medical products perform in diverse, real-world environments, reflecting the experiences of a wider range of patients and healthcare practices.

The benefits of real-world data compared to traditional RCT evidence

RWE can offer a richer perspective beyond what RCTs provide. Unlike RCTs, which are conducted under strict, controlled conditions with narrowly defined eligibility criteria, RWE captures the dynamic and diverse context in which treatments are used. This broader view provides a deeper understanding of how treatments perform in real-world settings, accounting for variations in patient populations. In essence, RWE bridges the gap between controlled clinical trials and routine practice. Here are four benefits of using RWD and RWE:

1. No strict eligibility criteria

RWE studies often have less stringent eligibility criteria compared to RCTs. This allows for the inclusion of patients with comorbidities or taking concomitant medications, which is more representative of real-world settings. This broader inclusion enhances the generalizability of RWE findings.

2. Quicker and more cost-effective

Conducting RWE studies is often quicker and more economical than RCTs. The use of pre-existing data from sources like EHRs eliminates many of the time-consuming and costly processes associated with patient recruitment and selection. This efficiency not only accelerates the research process but also can reduce overall costs, making it a more practical approach for some studies.

3. Enables research on high-risk groups

RWD enables research on high-risk groups, such as pregnant women and children, which can be difficult or ethically challenging to include in RCTs. This allows for a better understanding of treatment effects in vulnerable populations. Recognizing this, the FDA is increasingly promoting the use of RWD to better assess the safety and efficacy of medications used during pregnancy and lactation.

4. Allows tracking of real-world patient behavior

RWD provides the ability to track real-world patient behavior, including treatment adherence, outcomes, and the impact of interventions in everyday settings. This information is invaluable for understanding the effectiveness of treatments in real-world conditions. For instance, RWE studies can track how patients use medications, identify factors that influence adherence, and assess the long-term outcomes of treatments in diverse patient populations.

Sources of real-world data

Electronic health records (EHRs)

EHRs are a primary source of real-world data, offering a digital record of patient interactions with healthcare providers. EHRs capture a wealth of information, including diagnoses, treatments, medications, and follow-up care. This data provides insights into patient health trajectories, treatment efficacy, and disease management in everyday clinical settings.

Medical and prescription claims data

Claims data is another crucial source of real-world data. It offers a broad view of healthcare through detailed information on diagnoses, procedures, and prescriptions. This data can shed light on treatment patterns, adherence rates, and drug spending across large populations. Generally, claims databases capture a wide array of services-from inpatient and outpatient care to emergency room visits and pharmacy records. They track everything from clinic visits and surgeries to laboratory tests and hospital stays, painting a comprehensive picture of patient interactions with the healthcare system.

Patient registries and wearable devices

Patient registries and wearable devices offer valuable real-world insights into patient health and treatment outcomes. Patient registries collect data on specific diseases or conditions from a cohort of patients over time, often including detailed information on treatment responses and disease progression. Wearable devices, such as fitness trackers and smartwatches, provide continuous, real-time data on physical activity, vital signs, and other health metrics. Combined, these sources of data enhance our understanding of patient behavior, treatment adherence, and health outcomes outside the controlled environment of clinical trials.

Uses of real-world data and real-world evidence

Supporting regulatory decisions in drug approvals

The FDA has a long history of using RWE for post-market drug safety monitoring. However, the role of RWD and RWE is expanding. Today, we're seeing RWE play a bigger role in regulatory decision-making for drug and biological products, reflecting a growing recognition of its value in complementing traditional clinical trial data. By incorporating RWE, the FDA can gain a deeper understanding of a drug's performance in diverse populations, leading to more informed decisions and ensuring new therapies are both safe and effective for a broader range of patients. For instance, the FDA approved palbociclib, a breast cancer drug, for use in men based on EHR outcomes data related to its off-label use among men.

Performing pragmatic clinical trials

Pragmatic clinical trials (PCTs) test treatments in real-world scenarios, unlike traditional trials that use controlled, ideal conditions. PCTs thus show how interventions work in everyday practice, giving a clearer picture of their effectiveness and practicality for the general population. A notable example is the ADAPTABLE trial, the first large-scale, EHR-enabled trial in the U.S. It used EHR data to identify around 450,000 patients with cardiovascular disease and enrolled approximately 15,000 participants across 40 clinical centers. These participants were randomized to compare two aspirin dosage regimens and monitored for 3 to 6 months to find the best aspirin dose for cardiovascular disease, tracking deaths, hospitalizations for heart attacks, and hospitalizations for strokes. The ADAPTABLE trial was estimated to cost between one-fifth and one-half of a traditional RCT of a similar scale, highlighting the efficiency and real-world relevance of PCTs.

Enhancing patient recruitment with virtual control groups

Real-world data can also be used to improve the design and conduct of randomized controlled trials (RCTs), especially for rare diseases. Traditional RCTs with control groups can be challenging due to factors like enrollment difficulties, high costs, and lengthy timelines. In these cases, alternative designs, such as single-arm trials or synthetic control groups, can be considered. These designs use historical data or real-world data from EHRs to create a control group. While this approach has its limitations, it can be effective for diseases with well-understood progression.

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